Why Python is Popular in Generative AI
Walk into nearly any AI research lab, startup, or open-source project working on generative AI, and you'll find the same language underneath almost everything: Python. It's not the fastest language, and it wasn't originally designed with AI in mind. So why has it become the...
Self-Attention in Transformers
Self-attention is the mechanism that made transformers possible — and by extension, made models like GPT and Claude possible. It's been touched on in a few earlier posts in this series, but it deserves its own deep dive, since it's genuinely the core computational idea the...
Attention Mechanism Explained Simply
"Attention" is the single most important idea behind modern AI language models — so important that the paper that introduced transformers was literally titled "Attention Is All You Need." But the term itself can feel abstract. What does it actually mean for a computer program...
Transformer Architecture for Beginners
If there's one invention responsible for the current AI boom, it's the transformer. Introduced in a 2017 research paper titled "Attention Is All You Need," the transformer architecture is the foundation behind virtually every major LLM today — GPT, Claude, Gemini, Llama, all...
My GenAI Blogs
Why GenAI? Generative AI has completely changed how I think about software, creativity, and problem-solving. Over the past year, I've gone deep into the world of large language models, prompt engineering, retrieval-augmented generation, fine-tuning, and AI agents. The pace of...
Training vs Inference in LLMs
Every large language model has two very different lives: the phase where it learns, and the phase where it works. These are called training and inference, and while they're related, they're almost opposite in how they operate, what they cost, and what they're trying to...
What is an AI Inference?
You'll often hear phrases like "inference costs," "running inference," or "inference speed" when people talk about deploying AI models. It's one of those terms that sounds technical but describes something fairly simple: the moment an AI model actually gets used to produce an...
Parameters in Large Language Models
You've probably seen headlines like "GPT-4 has over a trillion parameters" or "this model has 7 billion parameters." But what actually is a parameter, and why does the number matter so much when people talk about AI capability? Let's break it down. What Is a Parameter,...
What is an LLM?
An LLM, or Large Language Model, is a type of artificial intelligence trained to understand and generate human language. It's the technology behind tools like ChatGPT, Claude, and Gemini — the systems that can answer questions, write essays, summarize documents, translate...
How Large Language Models Work
Large Language Models (LLMs) like GPT-4, Claude, and Gemini can write essays, debug code, and hold surprisingly natural conversations. But under the hood, they're not "thinking" the way humans do — they're performing an extraordinarily sophisticated version of pattern...
Generative AI vs Traditional AI
Artificial intelligence isn't one single thing — it's an umbrella term covering very different kinds of systems built for very different jobs. Two terms you'll hear constantly today are "traditional AI" and "generative AI." They're related, but they solve fundamentally...
What is Generative AI?
If you've typed a prompt into ChatGPT, asked Midjourney to paint a dragon riding a bicycle, or had an AI write code for you, you've used generative AI. But what actually makes it "generative," and how is it different from the AI that's been around for years? The Short Answer …